class_docstr: MOdular FActorial Design (MOFA).
  
  For more information on the algorithm, see original paper: MOFA: Modular
  Factorial Design for Hyperparameter Optimization https://arxiv.org/abs/2011.09545
  
  Xiong, Bo, Yimin Huang, Hanrong Ye, Steffen Staab, and Zhenguo Li.
  "MOFA: Modular Factorial Design for Hyperparameter Optimization."
  arXiv preprint arXiv:2011.09545 (2020).
  
  Notes
  -----
  Default values for the index, n_levels, and strength (t) parameter are set
  to the empirically obtained optimal values described in section 5.2 of the paper.
  
  The number of trials N for a single MOFA iteration is set to ``N = index * n_levels^t``.
  The ``--exp-max-trials`` should be a multiple of N.
  
  MOFA requires Python v3.8 or greater and scipy v1.8 or greater.
  
  Parameters
  ----------
  space: `orion.algo.space.Space`
      Optimisation space with priors for each dimension.
  seed: None, int or sequence of int
      Seed for the random number generator used to sample new trials.
      Default: ``None``
  index: int, optional
      This is the lambda parameter in the paper.
      Default: ``1``
  n_levels: int, optional
      Number of levels in the orthogonal Latin hypercube (OLH) table. Should be set to a
      prime number. This is the l parameter in the paper.
      Default: ``5``
  strength: int, optional
      Strength parameter. This is the t parameter in the paper.
      Default: ``2``
  threshold: float, optional
      The threshold to determine is a dimension was explored enough and can be fixed.
      Default: 0.1
docstr: Seed the state of the random number generator.
  
  Parameters
  ----------
  seed: int
      Integer seed for the random number generator.
docstr_tok: ["Seed","the","state","of","the","random","number","generator",".","Parameters","--","--","--","--","--","seed",":","int","Integer","seed","for","the","random","number","generator","."]
code: def seed_rng(self, seed: int) -> None:
      """Seed the state of the random number generator.
  
      Parameters
      ----------
      seed: int
          Integer seed for the random number generator.
  
      """
      self.rng = np.random.RandomState(seed)
  
imports: from __future__ import annotations
  import copy
  import logging
  from collections import defaultdict
  from typing import Sequence
  import numpy
  import pandas
  import scipy
  from packaging import version
  from orion.algo.base import BaseAlgorithm
  from orion.algo.mofa import sampler
  from orion.algo.mofa.transformer import Transformer
  from orion.algo.space import Real, Space
  from orion.core.utils.format_trials import dict_to_trial
  from orion.core.worker.trial import Trial
label_desc: Use image node_id 3 for calling the MOFA obj's underlying member method code with example usage: obj.seed_rng(seed) without return types